Knowledge information generating system

ABSTRACT

A knowledge information generating system including: a fundamental rule storage for making and storing, for each process data to be controlled, another process data relating to the former and the relations between these process data in advance as fundamental rules; a process data input processor for inputting and storing the process data to be controlled; a knowledge information collection necessity decider for detecting that the process data obtained from the process data input processor have changed or deviated from a predetermined relation; a knowledge information editor for fetching the fundamental rules relating to the process data, which are decided to be collected by the knowledge information collection necessity decider, from the fundamental rule storage, to edit the process data on the basis of the relations between the data group stored in the process data input processor and the process data stored in the fundamental rules; and a knowledge data base for storing the knowledge information obtained from the knowledge information editor.

TECHNICAL FIELD

The present invention relates to a system for automatically generatingknowledge information relating to the operations of a variety ofsubjects of control from information of the running state of thesubjects and, more particularly, to a system suitable for constructing aknowledge data base of a knowledge engineering applied expert systemwhich can contribute to the operation of a large-scale subject ofcontrol composed of complicated processes.

BACKGROUND ART

In recent years, there have been widely developed, in various fields, aknowledge engineering applied expert system which makes use of theconcept of AI (Artificial Intelligence). This system is intended toextract solutions or failures for an inquiry from the experiencesalready stored and to display and output them, or further, to control avariety of subjects by means of a computer. For realizing this system,the most serious problem resides in the difficulty of collecting pastexperiences of the experts or operators massively and accurately asknowledge. This cause will be examined in case the expert system isapplied to the operation of a subject of control. 1. For experiences ofmedium- or large-scale accidents, records are complete, and theoperator's memory is clear. Nevertheless, it is usual that small-scaleaccidents or minor events are neither recorded nor remembered clearly.2. The operator himself to supply the knowledge is not convinced thathis daily minor experiences are worth being stored in the computer sothat they may be used as the knowledge of the expert system.

Here, the medium- or small-scale accidents are experienced a little, andthese special events could not constitute an effective knowledge group.Since, moreover, the expert system to be applied to such a subjectshould ensure the operation without any accident, the knowledge shouldbe collected from the rather minor events to be experienced in dailyoperation, such as the small-scale accidents.

There are known in the prior art several methods for collectingknowledge dictating how the expert himself reacts and behaves againstthe situations set by the computer. For example, Japanese PatentLaid-Open No. 60 - 203576 discloses an invention which is directed to asystem for acquiring train rules for making a railway diagram. Theknowledge engineer or expert himself simulates and displays thesituation of an abnormal state (e.g., the disturbances of the diagram)by setting the abnormal state so that his knowledge may be smoothlyextracted as if he actually faces the practical problem. Japanese PatentLaid-Open No. 59 - 167771 discloses a system for acquiring knowledge bycreating "questions" for searching ultimate causes by use of theknowledge relations systemized by the structuring method and incooperation with the specialists. However, this system finds itdifficult to make the questions and is accompanied by a problem that itcannot acquire much knowledge information efficiently according to thereal operation of the subject of control.

DISCLOSURE OF INVENTION

Therefore, the present invention has an object to provide a knowledgeinformation generating system which can acquire much knowledgeinformation efficiently according to the real operation of the subjectof control.

In the present invention, the relations among the process data of asubject of control are grapsed in advance and stored as fundamentalrules, and these fundamental rules stored in connection with the processdata are extracted from fluctuations of the process data inputted fromthe actual subject so that the more detailed relations among the processdata are acquainted as knowledge from the change in the process datadescribed in the fudamental rules and the process data acting as thetrigger.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a schematic diagram showing the structure of the system of thepresent invention; FIGS. 2(a) and 2(b) are a schematic diagram showing aprocess data input processing portion; FIGS. 3(a) and 3(b) are a diagramshowing the stored situation of a buffer memory for storing the dataobtained by the processing portion of FIG. 2; FIGS. 4(a) and 4(b) are aflow chart describing the concept of a knowledge information collectionnecessity deciding portion; FIG. 5 is a table to be used for decidingthe discrepancy of the process data from the three-hold values; FIGS.6(a)-6(b), 7(a)-7(d), 8(a), 8(b), and 9 are diagrams showing theconcepts for making the fundamental rules; FIG. 10 is a schematicdiagram showing a thermal power plant which is exemplified forexplaining the concept of extracting the fundamental rules; FIG. 11 is atable enumerating the fundamental rules extracted in connection with thehigh-pressure heater set of the water-evaporation process of FIG. 10;FIG. 12 is a table enumerating the process data noting the fundamentalrules of FIG. 11, together with the correlating process data and theircorrelations; FIG. 13 is a processing flow chart of the knowledgeinformation editor 4; FIGS. 14(a), 14(b) and 14(c) are a diagram showingthe time series changes of the data described in the fundamental rules;FIG. 15 is a processing flow chart showing the knowledge informationadder 8; FIG. 16 is a processing flow chart showings the knowledgeinformation selector 9; FIG. 17 is a processing flow chart of thefundamental rule information processor 10; and FIG. 18 is a blockdiagram for explaining the editings using the knowledge information inthe data base.

BEST MODE FOR CARRYING OUT THE INVENTION

The present invention can be applied to a variety of subjects of controlsuch as subjects to be automatically or manually controlled, e.g.,thermal or nuclear power plants, iron making plants, industrial plantsor chemical plants. Vehicles to be automatically or manually controlledsuch as airplanes, vessels or electric cars are also covered by thecontrol subjects to which the present invention can be applied.

In the present invention, moreover, the process data of individualportions of the control subject are fetched every moment to generate theknowledge information. In a developed mode of application, the knowledgeinformation generated by the present invention is displayed through theoperator to the outside or directly to the control unit to operate thecontrol subject. In the various plants, a logger for recording theevents occurring is used for aiding in making the daily reports by theoperator. The events containing the correlations among the process datacan be recorded by making use of the present invention so that thepresent invention can be used as an automatic daily report maker.

Before entering into the description of the present invention, aschematic system structure of the invention will be described withreference to FIG. 1.

In FIG. 1, a portion 20 enclosed by single-dotted lines designates aknowledge information generating portion, which fetches the process datai of each portion of a subject of control 50 as its input informationthrough a process data input processor 2 so that the knowledgeinformation 13 of the so-called "if-then" form composed of a premise anda conclusion may be made and stored in a knowledge data base 11. Thisdata base 11 is coupled to a not-shown control unit or display to outputor display an output signal corresponding to the read command signalcoming from the control unit so that it may be used in the so-called"artificial intelligence type control or expert system" or a knowledgeengineering appliance. Moreover, the daily reports can be made from theoutputs of the running knowledge data base. The control subject to bedescribed hereinafter is exemplified by a plant, e.g., a thermal powerplant. Here, the data input processor 2 is shown in more detail in FIG.2, and the data structure obtained is shown in FIG. 3, as will bedescribed hereinafter.

In the knowledge information generating portion 20, a necessity decider3 for knowledge information collection judges the opportunity ofobtaining the knowledge information effective for the plant operationand feeds a collection necessary signal 7 to a knowledge informationeditor 4, if the analog data from the process data input processor 2deviates a normal range or if the digital data is changed from "1" to"0" or vice versa. When there is neither deviation from the normal rangenor any data change, the necessity decider 3 judges unnecessary 6 andstarts no subsequent processing. The program of the processing of thenecessity decider 3 is shown in FIG. 4, and the concept of deviation ofthe analog data is shown in FIG. 5.

Reference numeral 5 designates a process fundamental rule storage of theplant, which is recorded with the process fundamental rules known inadvance in association with the flows of fluids and heat, thecorrelations among the process data, the influence propagation routes ofthe process data and the states of the auxiliaries, so that the processdata are used as retrieval keys to read out the associated fundamentalrules to the outside. FIGS. 6 to 10 show the concepts of extracting thefundamental rules in advance; FIG. 11 shows one example of thefundamental rules extracted according to those concepts; and FIG. 12shows one example of the stored contents of the fundamental rule storage5. Incidentally, the fundamental rules are not imagined in advance butare selected from the knowledge made by the system of FIG. 1. Thefundamental rules are suitably extracted in a process fundamental ruleinformation processor 10 and are stored in the fundamental rule storage5.

The editor 4 is executed, if the collection is decided necessary by thenecessity decider 3, to use the process data decided necessary as thekey words so that it extracts the associated process data and thefundamental rules from the fundamental rule storage 5. A knowledge 16 isacquired by the processing of FIG. 13 in accordance with the processdata set and the fundamental rules. This knowledge 16 may be stored asit is as the knowledge 13 in the data base 11. Nevertheless, theknowledge 16 may have its propriety evaluated by the experts oroperators. For example, the expert's additional information 14 may beadded to the knowledge 16 by an information adder 8 to generate aknowledge 17. This knowledge 17 is not registered as the knowledge 13 inthe data base 11 in accordance with a select command 15 if it has beenfound in a knowledge information selector 9 that the knowledge 17 hasalready been registered in the data base 11 or is not effective as theknowledge.

The structures and operations of the individual units will be describedin detail in the following.

I: Process Data Input Processor 2

This processor has a schematic structure, as shown in FIG. 2. Of theprocess data of the plant, analog data i_(1A) to i_(nA) and i.sub.(n+1)Aare inputted to an analog input processor 100 so that their instantvalues are first sampled and held in synchronism with a sampling commandsignal T_(SH) in sample holders S/H. Here, the process data are for longand short periods and are sample-held for suitable periods, ifnecessary. The sample-held data are sequentially extracted bymultiplexers MPX and are converted to digital values by analog-digitalconverters AD until they are outputted from the analog input processor100 as signals 102 paired of the sample held values and the process datanames. On the other hand, the digital data i_(1D) to i_(nD) of theprocess data are inputted to a digital input processor 103, as shown inFIG. 2(b). It is detected by state change detectors 104 that the digitaldata have been changed from "1" to "0" or vice versa. Timers 105 outputsignals 106 in which the state change detection time, the data value (1or 0) and the process data names are paired.

The signals 102 or 106 thus detected are temporarily stored, as shown inFIG. 3, in a buffer memory disposed in the process data input processor2. In the case of the analog data, as shown in FIG. 3(a), the analogvalues at the individual sampling times (t=1, 2, - - - , j-1, j+1, - - -, m) are stored for each process data. In the sampling time t, t=m meansthe latest time, and t=1 means the earliest time. The signals 102 comingfrom the input unit 100 of FIG. 2 are stored in the t=m areas having thecorresponding process data names. The old data in the t=m areas areshifted to the (m-1) positions, and these shifts are accomplishedsequentially for all the data until the earliest data in the t=1 areasare finally disposed. For the data stored in the buffer memory, a series(t=1 to m) of the data having the names of the process data designatedcan be read out all at once, and the individual data can also be readout by designating the process data names and the times. FIG. 3 (b)shows the a buffer memory for storing the digital data such that thedigital data values and the state change detection times are stored inpairs for each process data. In this case, the data are stored in thebuffer memory only at the state change, and the data are extracted fromthe buffer memory by designating the process data name.

II: Necessity Decider 3 for Knowledge Information Collection

FIG. 4 presents a flow chart for describing the concept of thisnecessity decider. This flow chart is started by a knowledge makingcommand coming from either the operator or a not-shown suitable unit.First of all, at Block 110, the analog data I_(1A) (=I_(1A)(1)) at thetime t=1 is designated and is read out from the buffer memory of FIG.3(a).

In Blocks 111 to 116, the analog value I_(1A)(1) is compared with thethreshold value with reference to the Table of FIG. 5. The functions ofthese blocks will be described in the following. In case process data Xand Y have correlations, generally speaking, the process data Y shouldbe within a range determined by the data X. For example, if the data Yis in a proportional relation expressed by Y=kX (k: a constant), itsvalue should normally satisfy the range of kX-a<Y<kX+b (where a and bare constants). If the data Y deviates this range, it is possible toconsider that a singular event has occurred. Noting this singlarphenomenon of the process data, the present invention contemplatesgenerating knowledge effective for running the plant. The Blocks 111 to116 are used to judge the presence of the singular phenomenon of theanalog data, and Table shown in FIG. 5 is prepared for the judgement.This Table is stored, for all the analog data inputted, with thepresence of the correlations other process data, the correspondingprocess data names, if correlated, and the correlations.

The functions of the Blocks 111 to 116 will be described in more detailwith reference to FIG. 5. At the Block 111, it is judged from the Tableof FIG. 5 whether or not the input process data i_(1A) are correlated.If NO, the threshold value k (Kg/cm²) at the upper limit is fetched fromthe column of the relations of the Table of FIG. 5. At the Block 116,the threshold value k is compared with the process data i_(1A). At Block117, it is decided that all the analog data (from t=1 to t=m of FIG.3(a)) have been checked. If NOT, the data i_(1A) of a subsequent sampletime is extracted for t=t+1, and the processing of the Blocks 111, 115and 116 are repeated. If the data of t=m is executed for the datai_(1A), it is decided at the Block 118 whether or not there is adeviation from the threshold value. If YES, the necessity for theknowledge information collection in association with the process data issent to the knowledge information editor 4 at Block 119. At Block 120,it is confirmed that all the analog data have been judged. If NOT, anext process data is designated at Block 121, and the processings thusfar specified are repeated for t=0 at Block 122. Incidentally, theprocess data may have correlations, as i_(2A) or i_(4A), with otherprocess data. This case will be described in connection with the processdata of i_(2A). It is confirmed at the Block 112 with reference to theTable of FIG. 5 that the data i_(2A) is correlated with the data i.sub.10A and i_(13A). These data values i_(10A) and i_(13A) are fetched fromthe buffer memory (FIG. 3(a)) in the process data input processor 2 ofFIG. 1. At the Blocks 113, the relations of FIG. 5 are referred to inorder to calculate the threshold values (i.e., the upper limit m·i_(10A)and the lower limit (m-2)·i_(10A) for the data value i_(10A), and theupper limit n·i_(13A) +a and the lower limit n·i_(13A) -b for the datavalue i_(13A)). At the Block 114, the relations of (m=2)·i_(10A) <i_(2A)<m·i_(10A) are calculated with the value i_(10A), and the reltions ofn·i_(13A) -b<i_(2A) <n·i_(13A) +a are calculated with the value i_(13A).If the value i_(2A) deviated from those ranges, it is judged at theBlock 118 that the knowledge information collection is necessary, andthis necessary signal 7 is sent to the knowledge information editor 4 ofFIG. 1.

The procedures thus far described are directed to the case of the analogprocess data. The routine of FIG. 4 (b) is executed in the case of thedigital data. At first Block 123, the contents of the digital datai_(nD) stored in the buffer memory of FIG. 3(b) of the data inputprocessor 2 of FIG. 1 are read out. At Block 124, it is judged whetheror not the state is changed for a predetermined period (to the presenton and after the preceding knowledge edition, for example). If YES, thename and the change time of the data are sent out (at Block 125) to theinformation editor 4 of FIG. 1. If it is judged at the Block 124 thatthere was no change and if all the digital data are not judged at Block126, next digital data are checkd at Block 128. The editor 4 is drivenwhen all the analog data and all the digital data are confirmed, forexample.

III: Fundamental Rule Storage 5

It causes an abnormal phenomenon in the plant or process that the analogdata deviate from the threshold value or that the state is changed inthe digital data. The present invention acquires knowledge by noting theprocess data having caused the abnormal phenomena. More specifically,the relations between the abnormal process data and influencing (orinfluenced) process data or correlated process data (both of which willbe called together the "correlated process data") are extracted as muchas possible and are stored in advance in the fundamental rule storage 5.These fundamental rules are proportionally related between the processdata a and b, of which the data a are so abstract as are changed as aresult of the data b. The knowledge information editor 4 furthermodifies the fundamental rules to specify the constant of proportion orto determine the delay time between the process data. Before thedescription of the knowledge information editor 4, the fundamental rulestorage 5 will be described as follows.

Although the fundamental rules specify the relations between theabnormal process data and the correlated process data, as has beendescribed hereinbefore, it is not easy in fact to generate thefundamental rules in advance. The considerations from the followingpoints should be taken into the preparations of the fundamental rules.

A) The flow directions of the fluid and heat in the process should benoted:

The process never fails to have flow directions. In the ordinary plant,fluids such as water, steam, oil or gas and the flows of heat orelectricity are conceived, and the correlations following these flowsare ruled. The flow rates, pressures, temperatures and heats will bedescribed in the following. FIG. 6 shows the piping system taken up as asubject of control. A fluid source at a high temperature and under ahigh pressure is disposed at the lefthand side of FIG. 6, and a fludconsuming portion is branched at the righthand side. If the pipingsystem is thus simplified, the following relations are held among theflow rates, pressures and temperatures at the individual points I to IX.

Flow Rates: The summation of the inflow and outflow at the branch pointis always equal. As shown in FIG. 6 (b) , the flow rates Qa to Qe of theindividual portions are always satisfied by Qa=Qb+Qc and Qc=Qd+Qe. Incase the summation of the outflow is smaller than the inflow, thereoccurs an abnormal event that a leakage is caused upstream of the flowdetection point downstream of the branch point. In case the inflow issmaller, it informs that an abnormal event has caused another inflowfrom a different system.

Pressures: The detected pressure value (limited to the system of nopumping action) at upstream sides is always higher than that atdownstream sides because the pressure loss due to the flow velocity. Asshown in FIG. 6 (c), there hold the correlations of P₁ ≧P₂ ≧P₃ (P₃ ')and P₃ ≧P₄ ≧P₆. If these relations are not satisfied, the flowdirections are abnormal. Then, the flow rates are checked, and theabnormal information is produced.

Temperatures: A fluid never flows without exchanging heat with theexternal system. A heat inflow from the external system occurs in case acold fluid is handled, and a heat outflow into the external systemoccurs in case a hot fluid is handled. FIG. 6 (d) exemplifies the lattercase, in which there hold the correlations of T₁ ≧T₂ ≧T₃ (T₃ ') and T₃≧T₄ ≧T₆.

Heats: Even without any fluid medium, a temperature difference, if any,will never fail to cause the heat transfer in a direction to augment theentropy. In a plant constituting device made of a thick metal material,it is important to monitor the thermal stress due to the temperaturedifference between the inner and outer walls of the metal material. Theheat transfer can be deduced for each running condition, as shown inFIG. 6(e), and there hold the correlations of Ta>Tb, Tc>Tb, or Ta-Tb theset value, Tc-Tb<the set value. If these correlations are broken, it ispossible to produce information of the abnormality.

B) The correlations between the process data should be noted (withreference to FIG. 7):

The process data are essential for administering and controlling thequalities of the final process amounts to be produced in the plant.Important ones of the correlations among the primary and secondaryproduction process amounts for the material inputs, the specificparameters and the final process amounts are noted. For two process datahaving correlations such as linear, inversely proportional andsaturation characteristics, as shown in FIGS. 7(a) to 7(c), thedependent-variable process data Y corresponding to the measured valuesof the independent-variable process data X are tabulated together withthe correlations, if possible, in the tabular form of FIG. 7(d).

C) The influence propagation route of the process data should be noted(as shown in FIG. 8):

If some process data are noted, their relating other data are imagined.These imagined data to be considered are divided into two groups, i.e.,the data group acting as a source of influence propagations of theprocess data being noted and the data group acting as a target of theinfluence propagations.

D) The states of the auxiliaries should be noted (as shown in FIG. 9):

The running states of the plant and the individual auxiliaries areclosely related especially when the plant goes out of order. Therefore,the operating states of the individual auxiliaries in the normal andabnormal cases of the plant are noted and are edited into fundamentalrules individually or for each group. FIG. 9 exemplifies the cases inwhich the individual auxiliary (i.e., the valve A only) is checked andin case the two auxiliaries (i.e., the valves A and B) are checked.

Several concepts for extracting the fundamental rules of the plant havebeen explained hereinbefore. For the larger-scale plant, it is the moredifficult to extract the fundamental rules efficiently without anyexception. Next, a concept of extracting the fundamental rulesefficiently without any exception will be described in the following inconnection with a thermal power plant.

FIG. 10 is a schematic flow diagram showing the thermal power planttaken up as an example. In FIG. 10, steam generated by a boiler 201 issupplied through a main steampipe 218 to a high-pressure turbine 202, inwhich the heat energy of the steam is partially transformed intomechanically rotating energy for rotating a generator 204. The steamhaving worked in the high-pressure turbine 202 is heated again through acold reheating steam pipe 219 by a reheater 216 and is guided through ahot reheating steam pipe 220 into a reheating turbine 203 so that it maywork again. The steam having worked in the reheating turbine 203 flowsas an exhaust into a condenser 205 so that it is cooled into water bycooling water such as brine. The water thus condensed is pumped up by acondenser pump 106 so that its heat is recovered through individual heatexchangers such as a condensing heat exchanger 207, an air bleeder 208and a ground condenser 209. The condensed water is heated by alow-pressure water supply heater 210 and a bleeder 211. The water havingits pressure boosted by a boiler water supply pump 212 is further heatedby a high-pressure water supply heater 213 until it is fed to the boiler201 through a main water supply pipe 221. The water in the high-pressurewater supply heater 213, the bleeder 211 and the low-pressure watersupply heater 210 are heated by bleeding the turbine. In the boiler, onthe other hand, the fuel is controlled by a fuel regulator valve 217 andis burned with a necessary amount of air in a fuel burner 214. Thesupplied water is heated into steam by the radiation of the combustion,and this steam is overheated by an overheater 215 and sent to theturbine.

The thermal power plant can be expressed, if classified in connectionwith the process or the energy carrier, into four major processes, i.e.,the water-steam system 42, the air-gas-fuel system 41, theturbine-electric system 44 and the cooling water system 43. Theprocesses thus classified can be made to correspond to the components ofFIG. 10, i.e., the zones enclosed by single-dotted lines in FIG. 10. Thewater-steam system has a range containing the heat-exchanging functionof the boiler and the heat consuming function of the turbine in thesystem. This major process noting the flow of the substance or theenergy carrier can be further classified into several sub-processessharing the flow processes inclusive. In the water-steam system 42, forexample, there are enumerated the low-pressure heater group, thehigh-pressure heater group, the boiler and the turbine. Thesesub-processes contain the set of the devices for realizing the flowprocesses shared, and think of the set as a uni-process. By dividing thepower plant to the level of the auxiliaries, moreover, the whole plantcan be grasped by one side. Here, the devices in the case of thehigh-pressure heater are the high-pressure heater, the exit, the inputvalve, the bypass valve, and the bleeding valve.

Thus, the fundamental rules can be extracted efficiently without anyexception by classifying the whole power plant finely into the mainprocesses, sub-processes, the uni-processes and the devices and bysequentially extracting the relations between the fine levels or therank levels. FIG. 11 enumerates the events of the fundamental rules,which are extracted in connection with the high-pressure heater group ofthe water-steam system, and the relations among the classes of systemprocess, the fundamental rules extracted, and the concepts of ruleextraction, as has been described with reference to FIGS. 6 to 9. Thedescription will be described in the following with reference to FIG.11.

Event 1: Fundamental Rules of Device Level (Intrinsic to Devices)

These fundamental rules are related to the states of the auxiliaries ofthe fundamental rule extraction kind D and make it a rule that theheater bleeding valve is fully open at all times in the normal run ofthe plant.

Event 2: Fundamental Rules of Device Level (between Devices)

These fundamental rules are related to the states of the auxiliaries ofthe fundamental rule extraction kind D and make it a rule that the exitand entrance valves of the heater are equally open and closed oncondition that the devices belonging to one set of the high-pressureheater are correlated. A negligible time difference, if any, in the openstates of the two valves is eliminated.

Event 3: Fundamental Rules of Uni-Process level (in Uni-Processes)

These fundamental rules are composed of two kinds, i.e., those relatedto the states of the auxiliaries of the fundamental rule extraction kindD and those related to the flow direction of the process A. The formermakes it a rule that the exit/entrance valves are fully open whereas thebypass valve is fully closed in the normal run of the plant and that theexit/entrance valves are fully closed whereas the bypass valve is fullyopen in an expectable abnormal run of the plant. The latter makes it arule that the water supply at the heater exit is equal to that at theheater entrance at a constant heater water level.

Event 4: Fundamental Rules of Uni-Process level (between Uni-Processes)

These fundamental rules are related to the process flow direction of thefundamental rule extraction kind A and make it a rule that the supplywater temperature at the heater exit is lower than that of a subsequent(or downstream) stage and that the supply water pressure at the heaterexit is higher than that of a subsequent stage. These are rules basedupon the heat exchange and flow pressure loss in the water supply linesaround the high-pressure heater.

Event 5: Fundamental Rules of Sub-Process level (in Sub-Processes)

These fundamental rules are related to the process flow direction of thefundamental rule extraction kind A and make it a rule that the summationof bleed flows of high-pressure heaters is equal, at a constant waterlevel, to the drain flow of the final high-pressure heater. Thefundamental rules are also related to the correlations among the processdata of the fundamental rule extraction kind B and make it a rule thatthe summation of temperature rises of the high-pressure heaters islarger than a predetermined value. It is seen at one side that there isno heat balance in the total calories to be exchanged in thehigh-pressure heaters.

Event 6: Fundamental Rules of Sub-Process level (between Sub-Processes)

There is no rule to be especially provided.

Event 7: Fundamental Rules of Main Process level (in Main Processes)

These fundamental rules are related to correlations among the processdata of the fundamental rule extraction kind B and make it a rule thatlinear relations are present between the water supply at thehigh-pressure heater exit and the main steam flow. These rules monitorthe relations of the water and steam in the water-steam process from theview point of the plant performances.

Event 8: Fundamental Rules of Main Process level (between MainProcesses)

These fundamental rules are related to correlations among the processdata of the fundamental rule extraction kind C and make it a rule thatthe generator output may rise by 5% during the heater cut operation ofthe plant. This means that the correlations of the major data of theplant may be changed under a running condition in different processes.

As has been described hereinbefore, several kinds of fundamental ruleshave been enumerated in connection with the high-pressure heater. It is,however, unnecessary to make the rules in which the differences in andbetween the classified processes are indefinite or overlapped.

The individual fundamental rules thus extracted are tabulated in FIG. 12and stored in the fundamental rule unit of the automatic plant runninginformation generating system. FIG. 12 shows a portion of thefundamental rules extracted, as tabulated in FIG. 11, such that theabnormal process data, the related process data and the correlations arestored as one information set in the form of a defining Table. Forexample, the rule R₁ is the content described at "1" in FIG. 11, anddictates that the high-pressure heater bleeding flow I_(2a), the mastertrip relay reset signal I_(4d), the alarm reset signal I_(5d) and thedyanmo output signal I_(3a) should be monitored as the related processdata relating to a heater bleeder valve opening signal I_(1a) or theabnormal process data. The normal run is judged if I_(2a) and I_(3a) arelarger than predetermind values and if I_(4d) and I_(5d) are reset. Therules R₂ and R₃ are the contents described in "7" and "3" of FIG. 11 andare two independent rules which are determined in connection with thesupply water flow I_(4a) at the high-pressure heater exit. Of these, therule R₂ dictates that the main steam flow I_(5a), the main steampressure I_(6a) and the main steam temperature I_(7a) should bemonitored as the related process data, and that the relations of |I_(4a)-I_(5a) ×I_(6a) ×I_(7a) |≦C_(4a) ×I_(3a). Here, C_(4a) is a constant.The other rule R₃ dictates that the supply water flow I_(8a) at thehigh-pressure heater entrance and the water level I_(9a) of thehigh-pressure heater should be monitored as the related process data,and that the relations of |I_(4a) -I_(8a) |≦C_(9a) ×I_(9a) (C_(9a) : aconstant). Although not described in FIG. 12, the descriptions of thefundamental rules of FIG. 11 are made in relation to the process data.

IV: Knowledge Information Editor 4

The processings to be made here are based upon the processing flow chartof FIG. 13. This flow is started when the collection necessity signal 7is obtained from the knowledge information collection necessity decider(3 of FIG. 1), and the abnormal process data of the collection necessityare inputted (at Block 129). At Block 130, the related process data areretrieved with reference to the Table (of FIG. 12) of the fundamentalrule storage (5 of FIG. 1) of the plant. At Block 131, the abnormalprocess data and the related process data are fetched from the buffermemory (of FIG. 3) in the data input processor (2 of FIG. 1).Incidentally, in case a plurality of rules are set for one abnormalprocess data, one of them is fetched as the subject. In order to speedup the data fetch with reference to the various memories or tables, themain processes, subprocesses, uni-processes and devices should bedesignated at related input signal numbers. For example, the signals ofthe water-steam process may be designated at the order of 1,000; thesignals of the high-pressure heater set or the sub-processes at theorder of 1,100; and the signals of the high-pressure heater or theuni-processes at the order of 1,110.

At next Block 132, correlated process data described in the fundamentalrules are inputted from the Table (of FIG. 12) by using the abnormalprocess data as the retrieval signal. The propagating relationsdescribed in FIG. 8 are confirmed at Block 133. If NO, editings areexecuted at Block 134 on the basis of the extracted rules, as will bedescribed hereinafter. At Block 135, the results of editings for theindividual rules are individually outputted. At Block 136, it is judgedwhether or not the editings of all the rules stored in relation to theabnormal process data noted have been ended. If NO, a next rule is setat Block 137, and the processings of the Blocks 130 to 136 are executedagain. If YES, on the other hand, the processing of this abnormalprocess data is ended. If the data of the propagation target and sourceare stored in the noted rule at the Block 133, the edition (similar tothat of the Block 134) of Block 138 is executed, and the propagationtarget signal is set as new abnormal process data at Block 140. At Block141, the processings of the Blocks 130 to 133, 138 and 140 are executedfor all the propagation signals. At Block 142, the editings relating tothe propagations are outputted as a series of results. Thus, it ispossible to discriminate the events existing independently of oneanother and the events relating to one another and to output and graspthe plural influencing events as series ones.

Next, the processing contents of the editing Blocks 134 and 138 will bedescribed in the following. The editings are to measure the time periodsbetween the events occurring in the process data or to detect theoccurrences of the extreme values of the process data.

The event of edition will be described in connection with onehigh-pressure heater (i.e., uni-process) in the high-pressure heatergroup (i.e., sub-process) in the water-steam process of a thermal powerplant in case a bleeder valve for bleeding the high-pressure heater isfully closed by the manual operation of the operator. This operation isgenerally called the "heater cut". At the Block 129 of FIG. 13, inresponse to the digital data dictating the full closure of the bleedervalve, the information of the process data name and the correlationsrelating to the former are acquired from FIG. 12. The fundamental rulesaround the high-pressure heater are shown in FIG. 11. The rules relatingto the bleeder valve opening are the following three rules: thefundamental rules of the device level called the "heater bleeding valveopen fully in normal run"; the fundamental rules relating to theinfluence propagation route, i.e., "propagation source (e.g., thebleeder valve opening, main steam flow and the main steampressure)--abnormal phenomenon (e.g., the overload run of thegenerator)--propagation target (e.g., the stator winding temperature ofthe generator and the thrust differential expansion)"; and thefundamental rules of the "exit/entrance valves open fully and the bypassvalve open fully in the abnormal run". In the specific setting method ofthese three fundamental rules, for the first rules, the normal run isdefined in the form of a complementary set of the conditions of theindividual cases of conceivable abnormal run. The present case is duringthe "load run (Gen.MW=0) but not the trip or alarm. The bleeding flow iswithin a normal range". For the second rules, the overload running stateof the plant is thought as one abnormal state, the conceivable cause ofwhich is ruled to the propagation source and the conceivable result ofwhich is ruled to the propagation target. On the other hand, the thirdrules are made from the standpoint of the openings of the valvescorresponding to the running state. Here, the editing according to thefirst fundamental rules is called the "E1 processing", and the editingsaccording to the second and third fundamental rules are called the "E2and E3 processings". As a result of the acquisitions and inputs of thevarious process data from the Blocks 130 to 133 of FIG. 13, thefundamental rules necessary for the E1, E2 and E3 processings aredefined and called from the fundamental rule storage 5 to the knowledgeinformation editor 4 of FIG. 1. At this time, the content of thefundamental rules is copied and stored in the working area of theeditings so that the process data or the subjects of continuous monitorare accepted. The knowledge information editor 4 is given functions tocompute the agings of the individual process data to compare theintrinsic maximum, minimum, maximal and minimal values specified in thefundamental rules always with the measured process data so that the dataif deviated are handled as singular data, and to hold the knowledgeinformation in the editor while being edited. Other functions are topresent the correlations among the measured data processed in such aform as can be understood by the operator, e.g., the IF-THEN form, orAND (parallel) form, or the framing function to clarify the inclusion ofevents.

(1) (E1) Processing

The generator has a load and does not perform protections such as a tripor an alarm. For the fundamental rules that the heater bleeding valve isfully open while the heater bleeding flow is within the normal range,the states of the generator load a, the trip contact operating state b,the alarm contact operating state c, the heater bleeding amount d andthe heater bleeder valve opening e are collected at the E1 processor inthe editor by the full closure of the heater bleeder valve so that theiragings are observed.

FIG. 14 is a diagram presenting how the individual process datadescribed in the fundamental rules are changed in a time-series manner.According to the fundamental rules, all data should be at "1". Byobserving the procedure that the ideal states are not satisfied, thedevelopment of the abnormal phenomenon caused by the full closure of thehigh-pressure heater bleeder valve can be collected as knowledge. Here,the value "1" attached to the individual data a to e of FIG. 14 indicatethe following process data, respectively:

a: Generator load higher than a predetermined value - - - 1;

b: Trip contact operation (trip) - - - 1;

c: Alarm contact operation (alarm) - - - 1;

d: Heater bleed more than a predetermined value - - - 1; and

e: Heater bleeder valve more than a predetermined opening - - - 1.

FIG. 14(a) enumerates the states of the abnormal process data (i.e., theheater bleeder valve e in this example) and the related process datamerely in the time-series manner. Only the data of the time instants ofstate changes are extracted, as shown in FIG. 14(b), to constitute oneknowledge information. The knowledge information generated at this timeis expressed as it is in FIG. 14(c): "The heater bleeder valve is notfully open at time t, and the heater bleeding flow is lower after Δtthan the lower limit of the normal range. After 4Δt, the plant output=0,and the trip contact and the alarm contact are set. Since no statechange occurs after Δt', the edition is ended." This information isstored as the knowledge information obtained by the E1 processing,together with the data, day, time and generator power as referenceinformation. The processing of the fundamental rules having nopropagation is accomplished at the Block 134 of FIG. 13 andfundamentally in the route of the Blocks 129 to 137.

(2) E2 Processing

Like the E1 processing, the fundamental rules necessary for the E2processing are sought for and copied in the editor. Here, the abnormalstate of the plant is recognized by the load upon the generator (i.e.,the generator is loaded by the abnormal process data), and the bleedervalve opening is set as one item for the propagation source of theinfluence propagation route. If, in this case, the editing start istriggered by the load upon the generator, the causal relations seekingthe causes from the change of the phenomenon are specified. In the flowof FIG. 13, the Block 129 is started using the generator load as theabnormal process data, and the series processings of the Blocks 130 to137 are executed. If the fundamental rules of the propagations are foundin the processings, they are processed at the Block 138, and the bleedervalve opening or the propagation source is set at the Block 140 for thesubsequent repeating operations. As a result, the output at the Block142 is the series edited results from the bleeder valve opening to thegenerator load. In the foregoing example E1, the first cause is thefailure of the bleeder valve to be fully open, and the resultantsecondary and tertiary phenomena are grasped. Depending upon the settingmethod of the fundamenaal rules, therefore, the causal relations can besought for in two ways from both the causes and results.

If the heater bleeder valve opening of the first cause is graduallydirected from the full open to closed states, the editing is startedwhen the generator load exceeds the threshold value of the knowledgeinformation necessity at some time. Thus, the knowledge information tobe generated by E2 is that "the generator load is caused to rise X % ormore by the closure of the bleeder valve so that the stator windingtemperature of the generator also rises".

In the example E1, the input signal is a digital data and a statechanging signal, and the mutual time relations are noted for theedition. If the analog signal is included as in the example E2, the sizeand ratio of the analog data when there occurs an event (such as thestate change or the deviation of the analog data from a predeterminedvalue) are attained as one knowledge.

(3) E3 Processing

Like the E1 and E2 processings, the fundamental rules necessary for theE3 processing are sought for, and the abnormal run is defined, at thistime, as the full closure of the bleeder valve in a loaded run whileconsidering the heater cut operation. At this time, the time periodsfrom the full closure of the bleeder valve to the full closure of theheater inlet/outlet values and the full opening of the heater bypassvalve are so short that the knowledge information editor may beincapable of extracting the state changes. At this time, what isobtained is the knowledge information that the abnormal state iscontinuing. This is the information extracted because the process datahas no state change due to the dependency upon the order of the requiredoperation time. The cause and results of the changes can also be graspedby fetching the process data based upon the backward operations of thesupply device.

By the processings E1, E2 and E3, at least three knowledge informationsare generated, as described above. Since these have a common event astheir origin, there relatively arises a difference in the qualities ofthe knowledge informations. As the case may be, a complementaryknowledge information may hold so that it cannot be simply judged andautomatically selected. When all the informations cannot be inputted tothe knowledge data base, their quality can be improved by adding thejudgement of the operator.

V: Knowledge Information Adder 8

The running knowledge information generated in the procedures until IVcan be directly stored in the knowledge data base 11. The adder 8 isprovided for the corrections, supplementations and additions by theoperator for the information generated automatically. In the informationadder 8 shown in FIG. 1, the additional information 14 is inserted inthe following manner. As shown in FIG. 15, for example, the runningknowledge information from the information editor 4 is displayed in thedisplay such as the CRT so that the additional operation and therecognition result may be displayed in the case of the contentconfirmation and additional information by the operator. Another storagefunction is to call and recorrect the old and new knowledge informationsarbitrarily until the operator issues a transmission command to theprocessing function of the subsequent step, while the operator iscorrecting.

The necessity for this function will be described as follows. Therunning knowledge information generated in the procedure until theediting step IV is stored together with the major parameter such as thegeneration time or the load and the additional information such as thenumber of generation times in the running knowledge data base. Since, atthis time, the automatically generated knowledge information shouldfollow the initially inputted conditions (e.g., the conditions forgeneration the fundamental rules), it is necessary to manually input thereflections which are reminded by the operator. When the operatormanipulates the auxiliary machinery, this manipulation may not bediscriminated from the plant malfunction by the recognition at the sideof the present automatic knowledge information generating system. It isalso necessary for the operator to modify the generated knowledgeinformation. Thus, the quality of the knowledge information is improvedby adding the step of manually modifying the automatically generatedinformation to the procedure of the knowledge information generation.

In order to add the functions described above, the present system isequipped with: a display such as a CRT for displaying the automaticallygenerated knowledge information to the operator to confirm the contentof the knowledge information; and a control member such as a keyboardfor inputting the additional information of the operator to theinformation content. After this, the added and corrected knowledgeinformation can be redisplayed and recorrected so that its content issent, after confirmed, to the unit of the subsequent function, i.e., theknowledge information selector of the present system.

The knowledge addition will be specified in the following. Thehigh-pressure heater of the thermal power plant is exemplified by thecase in which the operator fully closes the bleed shut-off valve whileintending the run without the high-pressure heater. At this time, thepresent automatic knowledge information generating system recognizingthe state change of the full valve closure starts both the latch of theprocess data with reference to the fundamental rules and the knowledgeinformation generations. If, at this time, the change of the processdata corresponding to the manual operations is not incorporated into thefundamental rules, what is obtained as the knowledge information iseither the knowledge of the causal relations of the event caused thefull valve closure or the knowledge information, i.e., the manipulationor one item of the influence propagation source of the fundamental rulesrelating to the influence propagation route, but the definite elementssuch as the manipulations cannot be incorporated into the knowledgeinformation. For the operator, on the other hand, the change of theprocess data based upon his own manipulations is accepted as a clearfact. It is, therefore, possible at the present information adder 8 toadd the content that "the following process data change is caused as aresult of the manipulation" to the automatically generated knowledgeinformation. Another event is exemplified by the case in which theknowledge information is that "If the heater bleeder valve is closedduring the normal run, the operation is changed to an abnormal state sothat the generator output is increased and that the heater supply watertemperature drops." If the operator knows that the cause for the heaterbleeder valve closure comes from the malfunction based on the disorderof the controller, the operator adds one element block constituting theknowledge information that "the heater bleeder valve is brought out oforder by the malfunction of the controller." to the CRT display. As aresult, there can be attained as the added and corrected knowledgeinformation the content that "The controller goes out of order duringthe normal run so that the heater bleeder valve is erroneously closed toincrease the generator output and drop the heater supply watertemperature".

VI: Knowledge Information Selector 9

It is assumed that the confirmation of the generated knowledgeinformation by the operator is ended at the procedure V and that theknowledge information is qualified to be stored and constituted in theknowledge data base. In the case of the plant of long operation period,the knowledge information stored already may contain the same or similarone just generated. Some one has no meaning to be newly registered inthe data base. Therefore, the content of the plant operation knowledgedata base has to be freely called so that it may be compared andexamined with the knowledge information sent from the present knowledgeinformation selector 9. As to the knowledge information existing in thedata base and containing the individual process data constituting thenewly generated knowledge information, there is needed a function tocall the knowledge information automatically to the CRT display forcomparison. As shown in FIG. 16, therefore, the knowledge informationselections are accomplished with: the display function of the newlygenerated knowledge information on the CRT or the like; the simultaneousdisplay function of the knowledge information of the data base; thefunction of comparing the two; the updating function of the additionalinformation to the knowledge information; the selective latch functionof the knowledge information; the extraction function from and the latchfunction from the data base, and the elimination function. Here, theupdating function of the additional information to the knowledgeinformation is necessary for updating or latching the knowledgeinformation existing in the data base, when it is sufficient toadminister the latest generation timing of the knowledge information orthe number of the same knowledge information generations, in case thecontent of the generated knowledge information is identical to theknowledge information existing in the data base.

Next, in the example around the high-pressure heater of the thermalpower plant, if the event of the heater drain valve fully opened in anabnormal operation of high heater water level constitutes a portion ofone knowledge information, there arises no problem in case the runningstate of the plant then restores its normal state. In case thehysteresis of fully opening the drain valve is to be referred to clarifythe cause for and aid in making counter-measures for the turbine waterinductions, the number of fully opening the drain valve and the maximumvalue of the full open period are registered as the additional one ofthe knowledge information and are updated for the identical knowledgeinformation generation.

VII: Fundamental Rule Information Processor 10

The knowledge information to be stored in the knowledge data base 11 isclassified into two classes according to its generation method. One isgenerated by the present automatic knowledge information generatingsystem 20, and the other is generated and inputted to the knowledge database in advance by means other than the present system. The knowledgeinformation of the latter class may not be present in the least. Thepresent system is made so flexible as to follow the consistency as awhole while allowing the rational fundamental rules from the knowledgeinformation. The system improves the initially set fundamental rulesautomatically to improve the quality of the knowledge information storedin the data base finally. Specifically, the present fundamental ruleinformation processor 10 selects the out-of-date ones of the initiallyset fundamental rules, extracts the laws and causal relations of theknowledge information stored in the data base by the informationretrieval, and compares and checks the extracted result and the existingfundamental rules.

FIG. 17 shows one example of the processing method of the fundamentalrule information processor 10. FIG. 17 schematically presents theprocedures in case the fundamental rules are to be reversely generatedfrom the knowledge information 16, which is generated by the processdata edition referring to the fundamental rules, and the knowledgeinformation 16' which is inputted from the outside.

a) Knowledge Information 16 Generated by Present Automatic KnowledgeInformation Generating System 20

Since the plant running knowledge information 16 is generated by thepresent system 20 basically with reference to the initially setfundamental rules, none of the fundamental rules exceeding the initiallyset ones are caused in case the information is not worked by theoperator. In other words, the simplification and integration of therules are possible, but rules of different contents are not generatedwithout contradiction to the existing rules. In short, fundamental ruleshaving new meanings can be produced for the knowledge information whichis generated as a result of the information working by the operator.

The procedure for generating the new fundamental rules in this case willbe described in the following. First of all, as shown in FIG. 17, theknowledge information 16 in the knowledge data base is monitored (atBlock 200) at all times or for a predetermined period in connection withthe structural similarity of the knowledge information. The structure ofthe knowledge information is made by connecting a plurality of unitblocks, the connections (series or parallel) and inclusions (for eachsystem section) of which are monitored. In case the knowledgeinformation is constructed of three or more unit blocks, the structuralsimilarity is additionally retrieved in case one arbitrary block isdeficient. A hint is sought for generating the fundamental rules fromthe viewpoint that the block structure is not identical as a whole butis similar in a portion. In the structure classifying mechanismdescribed above, the similarity is checked, and the structuralsimilarities are grouped to count the number and frequency of thegroups. This counting operation is necessary for judging whether thefundamental rules are worth setting or stored as the knowledgeinformation of the special case. This comparison is accomplished bygiving a predetermined value in advance.

After it has been confirmed that the requisites for the fundamentalrules in the structural classifications, the intensive works and thenumber and frequency of the knowledge information are satisfied, thecommon factors are extracted (at 201) for each group, and coherentblocks are made (at 202) by the common factors. These common factorsneed not be common through the groups but may be those in a plurality ofcoherent blocks. Unless the fundamental rule judging portion by theoperator is provided, the frequency examination has to be accomplishedagain to make one of the fundamental rules. After this, each coherentblock is referred (at 203) to the content of the existing fundamentalrules. This is to confirm the difference between the generatingprocedure of the knowledge information generated by the automaticknowledge information generating system 20 and the existing fundamentalrules because the former comes from the latter. This difference, i.e.,the final condition for satisfying the new fundamental rules can beobtained by checking the resultant fundamental rules only.

After the ends of the processings at the aforementioned individualstages, a signal for requiring the decisions for the fundamental rulesis produced (at 204) so that the decisions may be assigned to theoperator. These decisions are accomplished by partially correcting orconfirming the candidates for new fundamental rules in the display suchas a CRT and constitute the root of the present system for automaticallygenerating the plant running knowledge information. The decisions aredesirably consulted by a plurality of operators for running the plant.

b) Knowledge Information Stored by External Input

The knowledge information stored in the knowledge data base by theexternal input is based upon the prerequisite that its content is whollytrue, and is sought for the fundamental rules. Basically, like the casea), the works of classifying the structures (at 206) of the blockelements, grouping, extracting the common factors (at 207) of theknowledge information in the structures of the same kind, coherentblocking (at 208) by the common factors, and counting the frequencies.Because of the knowledge information inputted from the outside, however,the rational check with the existing fundamental rules have to beaccomplished unlike the case (I). It is the best to check therationality of the whole knowledge information externally inputted atthe instant when it is stored in the knowledge data base. Since theautomatic knowledge information generating system may be started afterthe external input, the present fundamental rule information processoris given the rationality checking function. The knowledge informationsin the knowledge data base raise no problem even if they havecontradictory contents because they exert no influence on each other.When the knowledge informations are to be used for making thefundamental rules as in the present processor, the contradiction willraise serious adverse influences, and the erroneous knowledge should beprevented in advance. In case the externally inputted knowledgeinformations cannot always be assumed to be true, it is necessary, asdescribed above, to check the rationalities of all of the knowledgeinformations with the initially set fundamental rules. For thisnecessity, the rationality checking function of the present fundamentalrule information processor with the existing fundamental rules isenlarged as pre-processing at the instant of the external input of theknowledge data base.

The knowledge information, which has been edited and generated withreference to the initially set fundamental rules, is stored in theknowledge data base. As this storage increases, a new fundamental rulemaking operation functions so that the fundamental rule portion grows.

The high-pressure heater of the thermal power plant is taken up as aspecific example. A number of knowledge informations relating to that"if the heater bleeder valve is closed in the normal operation, theoperation turns abnormal, and the generator output may resultantlyincrease to drop the heater supply water temperature." are generated andstored in the knowledge data base, and their structural similarities arethen monitored. These knowledge informations have an omission of theirone component block "the generator output may increase", in the case ofthrottling the generator output automatically, and an addition of thecontent of the operator's operation that "the load limiting mode whenthe heater cut operation is selected" as their one component block. Ifthe following factors are extracted in the common factor extractingprocedures:

(A) "The heater cut operation is selected to establish the load limitingmode";

(B) "The heater bleeder valve is fully closed to shift the run to theabnormal one";

(C) "The heater water supply temperature drops"; and

(D) "The plant efficiency drops",

then these coherent factors provide one candidate for the fundamentalrules. After it has been confirmed that there is no identical one in theexisting rules, the request for judging the fundamental rules is issuedto the operator. The operator confirms the content through the displaysuch as the CRT and stores the item (B), from which the content"abnormal" is omitted, as the fundamental rules. The resultantfundamental rules have the content that "In case the heater cutoperation is selected, the load limiting mode comes into to fully closethe heater bleeder valve so that the heater supply water temperature isdropped together with the plant efficiency."

VIII: Referring Function of Knowledge Information Editings usingKnowledge Information in Knowledge Data Base

This is a function to merely store the knowledge information, which islatched in the plant running knowledge data base, or is utilized for theeditings described in the section IV in addition to the function togenerate the fundamental rules newly as in the section VII. In thisfunction, the knowledge information generated after the editings and theknowledge information in the knowledge data base are compared. If thiscomparison reveals the identity, the function is to update only theadditional information portion and store it in the data base or todispose it. If different, the procedure follows the normal processingroute, and the storage is made in the data base through the adding andselecting operations of the operator. This function is effective if themain purpose is to lighten the operational troubles of the operator.FIG. 18 shows an example of the structure in which the above-specifiedfunction is added to the knowledge information editor. In thehigh-pressure heater of the thermal power plant, for example, in casethe generation result of the knowledge information in the case oftransfer to the heater cut operation is identical to that of thepreceding generation, only the additional information is updated andstored directly in the data base.

Industrial Applicability

According to the present invention, the knowledge information can beacquired with ease by preparing the correlations and causal relationsamong the process data as the fundamental rules and by giving theinformation noting the events of the process data detected.

We claim:
 1. A knowledge information generating system comprising:meansfor storing, for each of first process data indicating a current stateof a subject of control to be monitored, second process data related tosaid first process data and fundamental rules indicating relationsbetween said first and second process data, said second process datarepresenting previously collected information concerning previous statesof said subject of control; means for fetching said fundamental rulesbased on said first process data indicating that a current state of thesubject of control is abnormal; and means for generating knowledgeinformation to be used to operate said subject of control based on saidfetched fundamental rules, said first process data input from saidsubject and said second process data.
 2. A knowledge informationgenerating system comprising:a fundamental rule storage device forstoring, for each of first process data indicating a current state of asubject of control to be monitored, second process data related to saidfirst process data and fundamental rules indicating relations betweensaid first and second process data, said second process datarepresenting previously collected information concerning previous statesof said subject of control; a process data input processor for inputtingsaid first process data from said subject of control; a knowledgeinformation collection necessity decider for deciding whether processdata for knowledge information generation is to be collected bydetecting whether said first process data input by said process datainput processor has either changed from a normal state or deviated froma predetermined relation between said first process data and said secondprocess data beyond a preset threshold value and outputting a necessarysignal when it is detected that said first process data has eitherchanged or deviated; a knowledge information editor for fetching saidfundamental rules and said process data from said fundamental rulestorage device in response to said necessary signal, editing saidprocess data based on relations between said first process data input bysaid process data input processor and said second process data indicatedby said fundamental rules, and generating knowledge information for usein operating said subject of control based on said edited second processdata; and a knowledge data base for storing said knowledge informationgenerated by said knowledge information editor.
 3. A knowledgeinformation generating system comprising:a fundamental rule storagedevice for storing, for each of first process data indicating a currentstate of a subject of control, second process data related to said firstprocess data and fundamental rules indicating relations between saidfirst and second process data, said second process data representingpreviously collected information concerning previous states of saidsubject of control; a process data input processor for inputting firstprocess data from said subject of control; a knowledge informationcollection necessity decider for deciding whether process data forknowledge information generation is to be collected by detecting whethersaid first process data input by said process data input processor haseither changed from a normal state or deviated from a predeterminedrelation between said first process data and said second process databeyond a preset threshold value and outputting a necessary signal whenit is detected that said first process data has either changed ordeviated; a knowledge information editor for fetching said fundamentalrules and said process data from said fundamental rule storage device inresponse to said necessary signal, editing said process data based onrelations between said first process data input by said process datainput processor and said second process data indicated by saidfundamental rules, and generating knowledge information for use inoperating said subject of control based on said edited second processdata; an information adder for adding additional information input by anoperator to said knowledge information generated by said knowledgeinformation editor; a knowledge data base for storing generatedknowledge information and said additional information input by saidoperator; and a fundamental rule information processor for correctingsaid fundamental rules, and storing said corrected fundamental rules insaid fundamental rule storage device.
 4. A fundamental rule correctingmethod comprising the steps of:storing, for each of first process dataindicating a current state of a subject of control to be monitored,second process data related to said first process data and fundamentalrules indicating relations between said first and second process data,said second process data representing previously collected informationconcerning previous states of said subject of control; extracting fromfirst process data input from said subject of control, pairs of firstprocess data having state values indicating a relation between said pairof first process data representing a physical quantity flow of saidsubject of control; comparing said extracted pairs of first process datato related pairs of second process data indicated by said fundamentalrules; and correcting fundamental rules based on said extracted pairs offirst process data and said related paris of second process data.
 5. Afundamental rule correcting method comprising the steps of:storing, foreach of first process data indicating a current state of a subject ofcontrol to be monitored, second process data related to said firstprocess data and fundamental rules indicating relations between saidfirst and second process data, said second process data representingpreviously collected information concerning previous states of saidsubject; extracting from first process data input from a subject ofcontrol, pairs of first process data having state values indicating afunctional relation between said pairs of first process data; comparingsaid extracted pairs of first process data to related pairs of secondprocess data indicated by said fundamental rules; and correcting saidfundamental rules based on said extracted pairs of first process dataand said related pairs of second process data.
 6. A fundamental rulecorrecting method comprising the steps of:storing, for each of firstprocess data indicating a current state of a subject of control to bemonitored, second process data related to said first process data andfundamental rules indicating relations between said first and secondprocess data, said second process data representing previously collectedinformation concerning previous states of said subject of control;extracting, from first process data input from said subject of control,pairs of first process data having state values indicating a source andtarget relation between said pairs of first process data representing aninfluence propagation route of a physical quantity of said subject ofcontrol; comparing said extracted pairs of first process data and saidsecond process data based on said fundamental rules; and correcting saidfundamental rules based on said extracted pairs of first process dataand related pairs of second process data.
 7. A fundamental rulecorrecting method comprising the steps of:storing, for each of firstprocess data indicating a current state of a subject of control to bemonitored, second process data related to said first process data andfundamental rules indicating relations between said first and secondprocess data, said second process data representing previously collectedinformation concerning previous states of said subject of control;extracting, from first process data input from said subject of control,pairs of first process data having state values indicating operationalrelations between a running state of said subject of control and anoperating state of auxiliary equipment of said subject of control;comparing said extracted pairs of first process data to related pairs ofsecond process data indicated by said fundamental rules; and correctingsaid fundamental rules based on said extracted pairs of first processdata and said related pairs of second process data.
 8. An expert systemfor controlling a subject of control by using knowledge informationeffective for running said subject of control, comprising:means forstoring, for each of first process data indicating a current state of asubject of control to be monitored, second process data related to saidfirst process data of said subject of control and fundamental rulesindicating relations between said first process data and said secondprocess data, said second process data representing previously collectedinformation concerning previous states of said subject of control; meansfor inputting first process data from said subject of control when saidsubject of control is running; means for generating knowledgeinformation based on said inputted first process data, said secondprocess data and said fundamental rules; means for displaying saidgenerated knowledge information for viewing by an operator of saidsubject of control; and means for applying said generated knowledgeinformation to a controller for controlling the running of said subjectof control.
 9. A logger system for outputting knowledge information foruse in controlling a subject of control, comprising:means for storing,for each of first process data indicating a current state of a subjectof control to be monitored, second process data related to said firstprocess data and information indicating relations between said first andsecond process data as fundamental rules, said second process datarepresenting previously collected information concerning previous statesof said subject of control; means for fetching said fundamental rulesbased on said first process data indicating that a current state of thesubject of control is abnormal; means for obtaining knowledgeinformation, which includes time data indicating time periods betweenchange of said first process data, based on said fetched fundamentalrules and said second process data; and means for outputting saidobtained knowledge information in a predetermined interval and ondemand.